1. Technical Field
The present teaching relates to methods and systems for providing content. Specifically, the present teaching relates to methods and systems for providing online content.
2. Discussion of Technical Background
The Internet has made it possible for a user to electronically access virtually any content at anytime and from any location. With the explosion of information, it has become more and more important to provide users with information that is relevant to the user and not just information in general. Further, as users of today's society rely on the Internet as their source of information, entertainment, and/or social connections, e.g., news, social interaction, movies, music, etc, it is critical to provide users with information they find valuable.
Efforts have been made to attempt to allow users to readily access relevant and on the point content. For example, topical portals have been developed that are more subject matter oriented as compared to generic content gathering systems such as traditional search engines. Example topical portals include portals on finance, sports, news, weather, shopping, music, art, film, etc. Such topical portals allow users to access information related to subject matters that these portals are directed to. Users have to go to different portals to access content of certain subject matter, which is not convenient and not user centric.
Another line of efforts in attempting to enable users to easily access relevant content is via personalization, which aims at understanding each user's individual likings/interests/preferences so that an individualized user profile for each user can be set up and can be used to select content that matches a user's interests. The underlying goal is to meet the minds of users in terms of content consumption. User profiles traditionally are constructed based on users' declared interests and/or inferred from, e.g., users' demographics. There have also been systems that identify users' interests based on observations made on users' interactions with content. A typical example of such user interaction with content is click through rate (CTR).
These traditional approaches have various shortcomings. For example, users' interests are profiled without any reference to a baseline so that the level of interest can be more accurately estimated. User interests are detected in isolated application settings so that user profiling in individual applications cannot capture a broad range of the overall interests of a user. Such traditional approach to user profiling lead to fragmented representation of user interests without a coherent understanding of the users' preferences. Because profiles of the same user derived from different application settings are often grounded with respect to the specifics of the applications, it is also difficult to integrate them to generate a more coherent profile that better represent the user's interests.
User activities directed to content are traditionally observed and used to estimate or infer users' interests. CTR is the most commonly used measure to estimate users' interests. However, CTR is no longer adequate to capture users' interests particularly given that different types of activities that a user may perform on different types of devices may also reflect or implicate user's interests. In addition, user reactions to content usually represent users' short term interests. Such observed short term interests, when acquired piece meal, as traditional approaches often do, can only lead to reactive, rather than proactive, services to users. Although short term interests are important, they are not adequate to enable understanding of the more persistent long term interests of a user, which are crucial in terms of user retention. Most user interactions with content represent short term interests of the user so that relying on such short term interest behavior makes it difficult to expand the understanding of the increasing range of interests of the user. When this is in combination with the fact that such collected data is always the past behavior and collected passively, it creates a personalization bubble, making it difficult, if not impossible, to discover other interests of a user unless the user initiates some action to reveal new interests.
Yet another line of effort to allow users to access relevant content is to pooling content that may be interested by users in accordance with their interests. Given the explosion of information on the Internet, it is not likely, even if possible, to evaluate all content accessible via the Internet whenever there is a need to select content relevant to a particular user. Thus, realistically, it is needed to identify a subset or a pool of the Internet content based on some criteria so that content can be selected from this pool and recommended to users based on their interests for consumption.
Conventional approaches to creating such a subset of content are application centric. Each application carves out its own subset of content in a manner that is specific to the application. For example, Amazon.com may have a content pool related to products and information associated thereof created/updated based on information related to its own users and/or interests of such users exhibited when they interact with Amazon.com. Facebook also has its own subset of content, generated in a manner not only specific to Facebook but also based on user interests exhibited while they are active on Facebook. As a user may be active in different applications (e.g., Amazon.com and Facebook) and with each application, they likely exhibit only part of their overall interests in connection with the nature of the application. Given that, each application can usually gain understanding, at best, of partial interests of users, making it difficult to develop a subset of content that can be used to serve a broader range of users' interests.
Another line of effort is directed to personalized content recommendation, i.e., selecting content from a content pool based on the user's personalized profiles and recommending such identified content to the user. Conventional solutions focus on relevance, i.e., the relevance between the content and the user. Although relevance is important, there are other factors that also impact how recommendation content should be selected in order to satisfy a user's interests. Most content recommendation systems insert advertisement to content identified for a user for recommendation. Some traditional systems that are used to identify insertion advertisements match content with advertisement or user's query (also content) with advertisement, without considering matching based on demographics of the user with features of the target audience defined by advertisers. Some traditional systems match user profiles with the specified demographics of the target audience defined by advertisers but without matching the content to be provided to the user and the advertisement. The reason is that content is often classified into taxonomy based on subject matters covered in the content yet advertisement taxonomy is often based on desired target audience groups. This makes it less effective in terms of selecting the most relevant advertisement to be inserted into content to be recommended to a specific user.
There is a need for improvements over the conventional approaches to personalizing content recommendation.